[Hiring] VP of Product @Xplor

Role Description

We are hiring a VP of Product to lead two strategic priorities: accelerating business performance through AI-driven innovation and unlocking new revenue opportunities through a data-as-a-service (DaaS) offering built on the wealth of data and insights across our platforms.

You will identify opportunities to simplify workflows, enhance productivity and improve how teams operate through the thoughtful adoption of AI agents and automation. Working collaboratively across the business, you'll build a network of AI champions to drive innovation, share best practices and scale adoption.

You'll also establish the frameworks, governance and enterprise controls needed to deploy AI solutions safely, responsibly and at scale, ensuring we build sustainable capabilities that deliver measurable value across Xplor.

You are an AI-native product leader: you build with AI yourself, measure everything, and hold automation and data products to clear quality, cost and risk standards.

Qualifications

• 12+ years in product management, with 5+ years leading product leaders in B2B SaaS or vertical software.
• A track record of taking data, AI, or ML products to market and showing revenue impact, not just pilots.
• AI-native builder: you prototype with AI coding and agent tools and use AI daily in your own work.
• Experience shipping AI-first products, including agentic workflows and human-in-the-loop design.
• Evaluation-driven product practice: building eval sets, setting quality thresholds and monitoring AI in production.
• Hands-on experience with pricing and packaging of AI features, including usage-based models and inference cost trade-offs.
• Working knowledge of generative AI, LLMs, agents, retrieval and evaluation, enough to challenge engineers and explain trade-offs to executives.
• Strong grounding in data foundations: quality, governance, privacy and what makes data AI-ready.
• Experience with responsible AI and regulatory frameworks across multiple regions.
• Proven ability to lead through influence in a multi-BU or federated organization.
• Clear communicator who can teach, simplify and present to executives and boards.

Requirements

• Build a network of AI champions.
• Recruit and train champions in every function and business unit to spot, build and own automations.
• Give champions playbooks, low-code tools, office hours and a clear path from idea to approved production workflow.
• Recognize and share wins so adoption spreads across teams.
• Lead change management: training, communications and support so people trust and use what gets built.
• Collaborate with champions to identify high-volume, repeatable processes across support, onboarding, finance, HR, sales operations, engineering and back office.
• Size each opportunity by hours saved, cost, cycle time and quality, and keep one ranked automation backlog for the company.
• Design AI-native workflows that create step function efficiency gains.
• Build reusable components (connectors, prompts, evals, templates) so each automation ships faster than the last.
• Set baselines and report savings and productivity gains against them.
• Define the operating model for agents and workflows: intake, approval, build, test, deploy, monitor and retire.
• Stand up a central registry so every agent has an owner, a purpose, the data and systems it can reach, and a risk tier.
• Partner with Security and IT on identity and least-privilege access for agents, secrets management, audit logs and kill switches.
• Set evaluation and monitoring standards: accuracy, drift, cost per task, failure rates and escalation to humans.
• Prevent sprawl: catch duplicates, enforce shared components, and retire agents that no longer earn their keep.
• Help functions manage AI spend with budgets, model routing and usage reporting by team.
• Turn systems of record data into insight products: industry benchmarks, spend and demand trends, cash-flow forecasts, risk signals and next-best-action recommendations.
• Define target customers (our SMB customers, enterprise partners, financial institutions or other third parties) and the problems each will pay to solve.
• Own pricing and packaging: subscriptions, tiers, usage-based APIs, embedded dashboards and premium add-ons.
• Work with Data Engineering to build AI-native data foundations: ingestion, transformation, normalization, categorization, entity resolution, quality and lineage.
• Use AI to make the data understandable: natural-language querying, summaries and explanations customers can act on.
• Take products from validation to launch, with go-to-market, enablement and customer success plans.
• Partner with Legal, Privacy, Security and Risk so every data product has a clear legal basis, consent and contract rights for the data it uses.
• Set standards for aggregation, anonymization and minimum cohort sizes so no customer, cardholder or individual can be identified.
• Keep payment card data out of scope for insight products and meet PCI DSS and data protection requirements in each region.
• Apply the same governance to internal automations that touch customer or employee data.

Benefits

• Competitive salary range: USD 280,000 - USD 290,000 yearly.